Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations209
Missing cells819
Missing cells (%)24.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory60.6 KiB
Average record size in memory296.7 B

Variable types

Text1
Categorical5
Numeric10

Alerts

ActiveCases is highly overall correlated with NewCases and 8 other fieldsHigh correlation
Continent is highly overall correlated with NewCases and 3 other fieldsHigh correlation
Deaths/1M pop is highly overall correlated with NewCases and 5 other fieldsHigh correlation
NewCases is highly overall correlated with ActiveCases and 13 other fieldsHigh correlation
NewDeaths is highly overall correlated with ActiveCases and 13 other fieldsHigh correlation
NewRecovered is highly overall correlated with ActiveCases and 13 other fieldsHigh correlation
Population is highly overall correlated with ActiveCases and 8 other fieldsHigh correlation
Serious,Critical is highly overall correlated with ActiveCases and 8 other fieldsHigh correlation
Tests/1M pop is highly overall correlated with Deaths/1M pop and 4 other fieldsHigh correlation
Tot Cases/1M pop is highly overall correlated with Deaths/1M pop and 6 other fieldsHigh correlation
TotalCases is highly overall correlated with ActiveCases and 9 other fieldsHigh correlation
TotalDeaths is highly overall correlated with ActiveCases and 9 other fieldsHigh correlation
TotalRecovered is highly overall correlated with ActiveCases and 9 other fieldsHigh correlation
TotalTests is highly overall correlated with ActiveCases and 8 other fieldsHigh correlation
WHO Region is highly overall correlated with Continent and 3 other fieldsHigh correlation
NewCases has 205 (98.1%) missing valuesMissing
TotalDeaths has 21 (10.0%) missing valuesMissing
NewDeaths has 206 (98.6%) missing valuesMissing
TotalRecovered has 4 (1.9%) missing valuesMissing
NewRecovered has 206 (98.6%) missing valuesMissing
ActiveCases has 4 (1.9%) missing valuesMissing
Serious,Critical has 87 (41.6%) missing valuesMissing
Deaths/1M pop has 22 (10.5%) missing valuesMissing
TotalTests has 18 (8.6%) missing valuesMissing
Tests/1M pop has 18 (8.6%) missing valuesMissing
WHO Region has 25 (12.0%) missing valuesMissing
NewCases is uniformly distributedUniform
NewDeaths is uniformly distributedUniform
NewRecovered is uniformly distributedUniform
Country/Region has unique valuesUnique
ActiveCases has 11 (5.3%) zerosZeros

Reproduction

Analysis started2025-11-30 19:08:49.051136
Analysis finished2025-11-30 19:09:02.113111
Duration13.06 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Country/Region
Text

Unique 

Distinct209
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size13.5 KiB
2025-11-30T20:09:02.348566image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length22
Median length18
Mean length8.3588517
Min length2

Characters and Unicode

Total characters1747
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique209 ?
Unique (%)100.0%

Sample

1st rowUSA
2nd rowBrazil
3rd rowIndia
4th rowRussia
5th rowSouth Africa
ValueCountFrequency (%)
and6
 
2.3%
islands4
 
1.5%
guinea3
 
1.2%
saint3
 
1.2%
new3
 
1.2%
netherlands2
 
0.8%
french2
 
0.8%
south2
 
0.8%
sudan2
 
0.8%
chile1
 
0.4%
Other values (232)232
89.2%
2025-11-30T20:09:02.810309image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a275
15.7%
n148
 
8.5%
i146
 
8.4%
e121
 
6.9%
r100
 
5.7%
o90
 
5.2%
t65
 
3.7%
u62
 
3.5%
s61
 
3.5%
l60
 
3.4%
Other values (46)619
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1747
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a275
15.7%
n148
 
8.5%
i146
 
8.4%
e121
 
6.9%
r100
 
5.7%
o90
 
5.2%
t65
 
3.7%
u62
 
3.5%
s61
 
3.5%
l60
 
3.4%
Other values (46)619
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1747
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a275
15.7%
n148
 
8.5%
i146
 
8.4%
e121
 
6.9%
r100
 
5.7%
o90
 
5.2%
t65
 
3.7%
u62
 
3.5%
s61
 
3.5%
l60
 
3.4%
Other values (46)619
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1747
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a275
15.7%
n148
 
8.5%
i146
 
8.4%
e121
 
6.9%
r100
 
5.7%
o90
 
5.2%
t65
 
3.7%
u62
 
3.5%
s61
 
3.5%
l60
 
3.4%
Other values (46)619
35.4%

Continent
Categorical

High correlation 

Distinct6
Distinct (%)2.9%
Missing1
Missing (%)0.5%
Memory size13.3 KiB
Africa
57 
Asia
48 
Europe
48 
North America
35 
South America
14 

Length

Max length17
Median length6
Mean length7.5048077
Min length4

Characters and Unicode

Total characters1561
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth America
2nd rowSouth America
3rd rowAsia
4th rowEurope
5th rowAfrica

Common Values

ValueCountFrequency (%)
Africa57
27.3%
Asia48
23.0%
Europe48
23.0%
North America35
16.7%
South America14
 
6.7%
Australia/Oceania6
 
2.9%
(Missing)1
 
0.5%

Length

2025-11-30T20:09:02.985381image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-30T20:09:03.121486image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
africa57
22.2%
america49
19.1%
asia48
18.7%
europe48
18.7%
north35
13.6%
south14
 
5.4%
australia/oceania6
 
2.3%

Most occurring characters

ValueCountFrequency (%)
r195
12.5%
a178
11.4%
i166
10.6%
A160
10.2%
c112
 
7.2%
e103
 
6.6%
o97
 
6.2%
u68
 
4.4%
f57
 
3.7%
t55
 
3.5%
Other values (12)370
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1561
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r195
12.5%
a178
11.4%
i166
10.6%
A160
10.2%
c112
 
7.2%
e103
 
6.6%
o97
 
6.2%
u68
 
4.4%
f57
 
3.7%
t55
 
3.5%
Other values (12)370
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1561
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r195
12.5%
a178
11.4%
i166
10.6%
A160
10.2%
c112
 
7.2%
e103
 
6.6%
o97
 
6.2%
u68
 
4.4%
f57
 
3.7%
t55
 
3.5%
Other values (12)370
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1561
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r195
12.5%
a178
11.4%
i166
10.6%
A160
10.2%
c112
 
7.2%
e103
 
6.6%
o97
 
6.2%
u68
 
4.4%
f57
 
3.7%
t55
 
3.5%
Other values (12)370
23.7%

Population
Real number (ℝ)

High correlation 

Distinct208
Distinct (%)100.0%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean30415487
Minimum801
Maximum1.381345 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2025-11-30T20:09:03.284305image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum801
5-th percentile45009.3
Q1966314
median7041972.5
Q325756136
95-th percentile1.1329838 × 108
Maximum1.381345 × 109
Range1.3813442 × 109
Interquartile range (IQR)24789822

Descriptive statistics

Standard deviation1.047661 × 108
Coefficient of variation (CV)3.4444985
Kurtosis135.1063
Mean30415487
Median Absolute Deviation (MAD)6748574.5
Skewness10.738089
Sum6.3264213 × 109
Variance1.0975936 × 1016
MonotonicityNot monotonic
2025-11-30T20:09:03.469002image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3311981301
 
0.5%
148838031
 
0.5%
50021001
 
0.5%
329563001
 
0.5%
34749561
 
0.5%
18839361
 
0.5%
102131381
 
0.5%
50686181
 
0.5%
458678521
 
0.5%
12082381
 
0.5%
Other values (198)198
94.7%
ValueCountFrequency (%)
8011
0.5%
34891
0.5%
49921
0.5%
262471
0.5%
336901
0.5%
339381
0.5%
381391
0.5%
387291
0.5%
387681
0.5%
392701
0.5%
ValueCountFrequency (%)
13813449971
0.5%
3311981301
0.5%
2738083651
0.5%
2212958511
0.5%
2127106921
0.5%
2066063001
0.5%
1648514011
0.5%
1459409241
0.5%
1290661601
0.5%
1264358591
0.5%

TotalCases
Real number (ℝ)

High correlation 

Distinct206
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91718.498
Minimum10
Maximum5032179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2025-11-30T20:09:03.650009image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile24.4
Q1712
median4491
Q336896
95-th percentile315323.8
Maximum5032179
Range5032169
Interquartile range (IQR)36184

Descriptive statistics

Standard deviation432586.68
Coefficient of variation (CV)4.7164606
Kurtosis92.197145
Mean91718.498
Median Absolute Deviation (MAD)4402
Skewness9.0577768
Sum19169166
Variance1.8713124 × 1011
MonotonicityDecreasing
2025-11-30T20:09:03.818990image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133
 
1.4%
252
 
1.0%
50321791
 
0.5%
12081
 
0.5%
18771
 
0.5%
17681
 
0.5%
16421
 
0.5%
15691
 
0.5%
14831
 
0.5%
13181
 
0.5%
Other values (196)196
93.8%
ValueCountFrequency (%)
101
 
0.5%
121
 
0.5%
133
1.4%
141
 
0.5%
171
 
0.5%
181
 
0.5%
201
 
0.5%
221
 
0.5%
241
 
0.5%
252
1.0%
ValueCountFrequency (%)
50321791
0.5%
29175621
0.5%
20254091
0.5%
8718941
0.5%
5381841
0.5%
4626901
0.5%
4554091
0.5%
3666711
0.5%
3577101
0.5%
3545301
0.5%

NewCases
Categorical

High correlation  Missing  Uniform 

Distinct4
Distinct (%)100.0%
Missing205
Missing (%)98.1%
Memory size11.6 KiB
6590.0
1282.0
20.0
30.0

Length

Max length6
Median length5
Mean length5
Min length4

Characters and Unicode

Total characters20
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row6590.0
2nd row1282.0
3rd row20.0
4th row30.0

Common Values

ValueCountFrequency (%)
6590.01
 
0.5%
1282.01
 
0.5%
20.01
 
0.5%
30.01
 
0.5%
(Missing)205
98.1%

Length

2025-11-30T20:09:03.989442image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-30T20:09:04.122943image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
6590.01
25.0%
1282.01
25.0%
20.01
25.0%
30.01
25.0%

Most occurring characters

ValueCountFrequency (%)
07
35.0%
.4
20.0%
23
15.0%
61
 
5.0%
51
 
5.0%
91
 
5.0%
11
 
5.0%
81
 
5.0%
31
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)20
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07
35.0%
.4
20.0%
23
15.0%
61
 
5.0%
51
 
5.0%
91
 
5.0%
11
 
5.0%
81
 
5.0%
31
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)20
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07
35.0%
.4
20.0%
23
15.0%
61
 
5.0%
51
 
5.0%
91
 
5.0%
11
 
5.0%
81
 
5.0%
31
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)20
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07
35.0%
.4
20.0%
23
15.0%
61
 
5.0%
51
 
5.0%
91
 
5.0%
11
 
5.0%
81
 
5.0%
31
 
5.0%

TotalDeaths
Real number (ℝ)

High correlation  Missing 

Distinct150
Distinct (%)79.8%
Missing21
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean3792.5904
Minimum1
Maximum162804
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2025-11-30T20:09:04.282187image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.35
Q122
median113
Q3786
95-th percentile16796.5
Maximum162804
Range162803
Interquartile range (IQR)764

Descriptive statistics

Standard deviation15487.185
Coefficient of variation (CV)4.0835374
Kurtosis67.248526
Mean3792.5904
Median Absolute Deviation (MAD)106.5
Skewness7.5304606
Sum713007
Variance2.398529 × 108
MonotonicityNot monotonic
2025-11-30T20:09:04.461524image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17
 
3.3%
35
 
2.4%
154
 
1.9%
473
 
1.4%
223
 
1.4%
23
 
1.4%
103
 
1.4%
53
 
1.4%
73
 
1.4%
273
 
1.4%
Other values (140)151
72.2%
(Missing)21
 
10.0%
ValueCountFrequency (%)
17
3.3%
23
1.4%
35
2.4%
41
 
0.5%
53
1.4%
61
 
0.5%
73
1.4%
81
 
0.5%
92
 
1.0%
103
1.4%
ValueCountFrequency (%)
1628041
0.5%
986441
0.5%
505171
0.5%
464131
0.5%
416381
0.5%
351871
0.5%
303121
0.5%
285001
0.5%
204241
0.5%
179761
0.5%

NewDeaths
Categorical

High correlation  Missing  Uniform 

Distinct3
Distinct (%)100.0%
Missing206
Missing (%)98.6%
Memory size11.6 KiB
819.0
80.0
1.0

Length

Max length5
Median length4
Mean length4
Min length3

Characters and Unicode

Total characters12
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row819.0
2nd row80.0
3rd row1.0

Common Values

ValueCountFrequency (%)
819.01
 
0.5%
80.01
 
0.5%
1.01
 
0.5%
(Missing)206
98.6%

Length

2025-11-30T20:09:04.639813image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-30T20:09:04.769957image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
819.01
33.3%
80.01
33.3%
1.01
33.3%

Most occurring characters

ValueCountFrequency (%)
04
33.3%
.3
25.0%
82
16.7%
12
16.7%
91
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)12
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
04
33.3%
.3
25.0%
82
16.7%
12
16.7%
91
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
04
33.3%
.3
25.0%
82
16.7%
12
16.7%
91
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
04
33.3%
.3
25.0%
82
16.7%
12
16.7%
91
 
8.3%

TotalRecovered
Real number (ℝ)

High correlation  Missing 

Distinct201
Distinct (%)98.0%
Missing4
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean58878.98
Minimum7
Maximum2576668
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2025-11-30T20:09:04.932366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile22.2
Q1334
median2178
Q320553
95-th percentile241780.4
Maximum2576668
Range2576661
Interquartile range (IQR)20219

Descriptive statistics

Standard deviation256698.41
Coefficient of variation (CV)4.3597631
Kurtosis65.589296
Mean58878.98
Median Absolute Deviation (MAD)2156
Skewness7.7420048
Sum12070191
Variance6.5894072 × 1010
MonotonicityNot monotonic
2025-11-30T20:09:05.241326image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
462
 
1.0%
242
 
1.0%
182
 
1.0%
19542
 
1.0%
25766681
 
0.5%
15241
 
0.5%
5201
 
0.5%
10791
 
0.5%
10701
 
0.5%
11711
 
0.5%
Other values (191)191
91.4%
(Missing)4
 
1.9%
ValueCountFrequency (%)
71
0.5%
81
0.5%
101
0.5%
121
0.5%
131
0.5%
141
0.5%
161
0.5%
182
1.0%
191
0.5%
221
0.5%
ValueCountFrequency (%)
25766681
0.5%
20476601
0.5%
13773841
0.5%
6763571
0.5%
3873161
0.5%
3401681
0.5%
3103371
0.5%
3088481
0.5%
2774631
0.5%
2560581
0.5%

NewRecovered
Categorical

High correlation  Missing  Uniform 

Distinct3
Distinct (%)100.0%
Missing206
Missing (%)98.6%
Memory size11.6 KiB
4140.0
936.0
42.0

Length

Max length6
Median length5
Mean length5
Min length4

Characters and Unicode

Total characters15
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row4140.0
2nd row936.0
3rd row42.0

Common Values

ValueCountFrequency (%)
4140.01
 
0.5%
936.01
 
0.5%
42.01
 
0.5%
(Missing)206
98.6%

Length

2025-11-30T20:09:05.411304image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-30T20:09:05.544622image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
4140.01
33.3%
936.01
33.3%
42.01
33.3%

Most occurring characters

ValueCountFrequency (%)
04
26.7%
43
20.0%
.3
20.0%
11
 
6.7%
91
 
6.7%
31
 
6.7%
61
 
6.7%
21
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)15
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
04
26.7%
43
20.0%
.3
20.0%
11
 
6.7%
91
 
6.7%
31
 
6.7%
61
 
6.7%
21
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
04
26.7%
43
20.0%
.3
20.0%
11
 
6.7%
91
 
6.7%
31
 
6.7%
61
 
6.7%
21
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
04
26.7%
43
20.0%
.3
20.0%
11
 
6.7%
91
 
6.7%
31
 
6.7%
61
 
6.7%
21
 
6.7%

ActiveCases
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct180
Distinct (%)87.8%
Missing4
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean27664.327
Minimum0
Maximum2292707
Zeros11
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2025-11-30T20:09:05.698848image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q186
median899
Q37124
95-th percentile77405.8
Maximum2292707
Range2292707
Interquartile range (IQR)7038

Descriptive statistics

Standard deviation174632.74
Coefficient of variation (CV)6.3125605
Kurtosis142.10258
Mean27664.327
Median Absolute Deviation (MAD)898
Skewness11.370144
Sum5671187
Variance3.0496593 × 1010
MonotonicityNot monotonic
2025-11-30T20:09:05.868715image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
011
 
5.3%
16
 
2.9%
24
 
1.9%
1072
 
1.0%
2022
 
1.0%
742
 
1.0%
332
 
1.0%
97582
 
1.0%
62
 
1.0%
272
 
1.0%
Other values (170)170
81.3%
(Missing)4
 
1.9%
ValueCountFrequency (%)
011
5.3%
16
2.9%
24
 
1.9%
31
 
0.5%
41
 
0.5%
62
 
1.0%
81
 
0.5%
91
 
0.5%
101
 
0.5%
121
 
0.5%
ValueCountFrequency (%)
22927071
0.5%
7712581
0.5%
6063871
0.5%
1809311
0.5%
1534161
0.5%
1412641
0.5%
1246481
0.5%
1240921
0.5%
1033251
0.5%
1025211
0.5%

Serious,Critical
Real number (ℝ)

High correlation  Missing 

Distinct74
Distinct (%)60.7%
Missing87
Missing (%)41.6%
Infinite0
Infinite (%)0.0%
Mean534.39344
Minimum1
Maximum18296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2025-11-30T20:09:06.029671image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.25
median27.5
Q3160.25
95-th percentile2245.6
Maximum18296
Range18295
Interquartile range (IQR)157

Descriptive statistics

Standard deviation2047.5186
Coefficient of variation (CV)3.8314815
Kurtosis50.557418
Mean534.39344
Median Absolute Deviation (MAD)25.5
Skewness6.6184787
Sum65196
Variance4192332.5
MonotonicityNot monotonic
2025-11-30T20:09:06.206867image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112
 
5.7%
212
 
5.7%
37
 
3.3%
54
 
1.9%
74
 
1.9%
234
 
1.9%
43
 
1.4%
243
 
1.4%
423
 
1.4%
93
 
1.4%
Other values (64)67
32.1%
(Missing)87
41.6%
ValueCountFrequency (%)
112
5.7%
212
5.7%
37
3.3%
43
 
1.4%
54
 
1.9%
61
 
0.5%
74
 
1.9%
81
 
0.5%
93
 
1.4%
111
 
0.5%
ValueCountFrequency (%)
182961
0.5%
89441
0.5%
83181
0.5%
41561
0.5%
39871
0.5%
23001
0.5%
22631
0.5%
19151
0.5%
14931
0.5%
14261
0.5%

Tot Cases/1M pop
Real number (ℝ)

High correlation 

Distinct202
Distinct (%)97.1%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean3196.024
Minimum3
Maximum39922
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2025-11-30T20:09:06.374457image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile31.05
Q1282
median1015
Q33841.75
95-th percentile13766.05
Maximum39922
Range39919
Interquartile range (IQR)3559.75

Descriptive statistics

Standard deviation5191.9865
Coefficient of variation (CV)1.6245142
Kurtosis15.63126
Mean3196.024
Median Absolute Deviation (MAD)945.5
Skewness3.3942965
Sum664773
Variance26956723
MonotonicityNot monotonic
2025-11-30T20:09:06.548330image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82
 
1.0%
4542
 
1.0%
8022
 
1.0%
1362
 
1.0%
2192
 
1.0%
572
 
1.0%
3141
 
0.5%
451
 
0.5%
3791
 
0.5%
6771
 
0.5%
Other values (192)192
91.9%
ValueCountFrequency (%)
31
0.5%
71
0.5%
82
1.0%
151
0.5%
171
0.5%
181
0.5%
191
0.5%
201
0.5%
271
0.5%
301
0.5%
ValueCountFrequency (%)
399221
0.5%
271461
0.5%
251301
0.5%
205961
0.5%
191651
0.5%
165271
0.5%
163781
0.5%
157691
0.5%
151941
0.5%
149811
0.5%

Deaths/1M pop
Real number (ℝ)

High correlation  Missing 

Distinct107
Distinct (%)57.2%
Missing22
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean98.681176
Minimum0.08
Maximum1238
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2025-11-30T20:09:06.745132image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.59
Q16
median29
Q398
95-th percentile483.6
Maximum1238
Range1237.92
Interquartile range (IQR)92

Descriptive statistics

Standard deviation174.95686
Coefficient of variation (CV)1.7729507
Kurtosis12.408189
Mean98.681176
Median Absolute Deviation (MAD)25
Skewness3.1401163
Sum18453.38
Variance30609.904
MonotonicityNot monotonic
2025-11-30T20:09:06.918037image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
611
 
5.3%
48
 
3.8%
57
 
3.3%
36
 
2.9%
75
 
2.4%
205
 
2.4%
85
 
2.4%
475
 
2.4%
24
 
1.9%
154
 
1.9%
Other values (97)127
60.8%
(Missing)22
 
10.5%
ValueCountFrequency (%)
0.081
 
0.5%
0.13
 
1.4%
0.32
 
1.0%
0.42
 
1.0%
0.52
 
1.0%
0.82
 
1.0%
12
 
1.0%
24
1.9%
36
2.9%
48
3.8%
ValueCountFrequency (%)
12381
0.5%
8501
0.5%
6831
0.5%
6731
0.5%
6191
0.5%
6101
0.5%
5821
0.5%
5711
0.5%
5171
0.5%
4921
0.5%

TotalTests
Real number (ℝ)

High correlation  Missing 

Distinct190
Distinct (%)99.5%
Missing18
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean1402404.7
Minimum61
Maximum63139605
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2025-11-30T20:09:07.103321image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile1217.5
Q125752
median135702
Q3757696
95-th percentile4856610.5
Maximum63139605
Range63139544
Interquartile range (IQR)731944

Descriptive statistics

Standard deviation5553366.7
Coefficient of variation (CV)3.9598888
Kurtosis85.180632
Mean1402404.7
Median Absolute Deviation (MAD)132016
Skewness8.4910359
Sum2.678593 × 108
Variance3.0839881 × 1013
MonotonicityNot monotonic
2025-11-30T20:09:07.274154image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15002
 
1.0%
631396051
 
0.5%
6287451
 
0.5%
936771
 
0.5%
1496931
 
0.5%
1201
 
0.5%
1002981
 
0.5%
4869431
 
0.5%
647471
 
0.5%
1269561
 
0.5%
Other values (180)180
86.1%
(Missing)18
 
8.6%
ValueCountFrequency (%)
611
0.5%
1201
0.5%
4011
0.5%
4241
0.5%
9001
0.5%
10051
0.5%
10801
0.5%
11151
0.5%
11461
0.5%
11831
0.5%
ValueCountFrequency (%)
631396051
0.5%
297169071
0.5%
221493511
0.5%
175152341
0.5%
132061881
0.5%
85866481
0.5%
70997131
0.5%
70643291
0.5%
52626581
0.5%
50818021
0.5%

Tests/1M pop
Real number (ℝ)

High correlation  Missing 

Distinct190
Distinct (%)99.5%
Missing18
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean83959.366
Minimum4
Maximum995282
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2025-11-30T20:09:07.446608image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile1401
Q18956.5
median32585
Q392154.5
95-th percentile366419.5
Maximum995282
Range995278
Interquartile range (IQR)83198

Descriptive statistics

Standard deviation152730.59
Coefficient of variation (CV)1.8191013
Kurtosis17.116415
Mean83959.366
Median Absolute Deviation (MAD)27685
Skewness3.8516447
Sum16036239
Variance2.3326634 × 1010
MonotonicityNot monotonic
2025-11-30T20:09:07.627983image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
305462
 
1.0%
1906401
 
0.5%
62871
 
0.5%
77091
 
0.5%
4383851
 
0.5%
41
 
0.5%
84781
 
0.5%
973481
 
0.5%
19651
 
0.5%
365351
 
0.5%
Other values (180)180
86.1%
(Missing)18
 
8.6%
ValueCountFrequency (%)
41
0.5%
91
0.5%
3731
0.5%
7611
0.5%
10751
0.5%
10941
0.5%
12061
0.5%
12391
0.5%
13101
0.5%
13171
0.5%
ValueCountFrequency (%)
9952821
0.5%
9729821
0.5%
8805901
0.5%
6845651
0.5%
5314701
0.5%
5204931
0.5%
5136911
0.5%
4727801
0.5%
4383851
0.5%
4232981
0.5%

WHO Region
Categorical

High correlation  Missing 

Distinct6
Distinct (%)3.3%
Missing25
Missing (%)12.0%
Memory size13.4 KiB
Europe
55 
Africa
47 
Americas
35 
EasternMediterranean
22 
WesternPacific
15 

Length

Max length20
Median length6
Mean length9.1413043
Min length6

Characters and Unicode

Total characters1682
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAmericas
2nd rowAmericas
3rd rowSouth-EastAsia
4th rowEurope
5th rowAfrica

Common Values

ValueCountFrequency (%)
Europe55
26.3%
Africa47
22.5%
Americas35
16.7%
EasternMediterranean22
 
10.5%
WesternPacific15
 
7.2%
South-EastAsia10
 
4.8%
(Missing)25
12.0%

Length

2025-11-30T20:09:07.798401image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-30T20:09:07.928796image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
europe55
29.9%
africa47
25.5%
americas35
19.0%
easternmediterranean22
 
12.0%
westernpacific15
 
8.2%
south-eastasia10
 
5.4%

Most occurring characters

ValueCountFrequency (%)
r218
13.0%
e208
12.4%
a183
10.9%
i144
 
8.6%
c112
 
6.7%
A92
 
5.5%
s92
 
5.5%
E87
 
5.2%
n81
 
4.8%
t79
 
4.7%
Other values (12)386
22.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)1682
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r218
13.0%
e208
12.4%
a183
10.9%
i144
 
8.6%
c112
 
6.7%
A92
 
5.5%
s92
 
5.5%
E87
 
5.2%
n81
 
4.8%
t79
 
4.7%
Other values (12)386
22.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1682
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r218
13.0%
e208
12.4%
a183
10.9%
i144
 
8.6%
c112
 
6.7%
A92
 
5.5%
s92
 
5.5%
E87
 
5.2%
n81
 
4.8%
t79
 
4.7%
Other values (12)386
22.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1682
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r218
13.0%
e208
12.4%
a183
10.9%
i144
 
8.6%
c112
 
6.7%
A92
 
5.5%
s92
 
5.5%
E87
 
5.2%
n81
 
4.8%
t79
 
4.7%
Other values (12)386
22.9%

Interactions

2025-11-30T20:08:59.928539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:49.970351image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:50.870604image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:51.911051image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:53.331116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:54.480074image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:55.584373image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:56.736471image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:58.075165image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:58.971717image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:09:00.030865image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:50.106001image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:50.958529image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:52.047711image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:53.444856image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:54.598845image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:55.702938image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:56.873253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:58.192669image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:59.054584image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:09:00.154090image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:50.183930image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:51.028433image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:52.171756image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:53.557719image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:54.710904image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:55.829414image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:57.107768image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:58.309870image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:59.132986image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:09:00.285789image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:50.271182image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:51.130598image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:52.461835image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:53.692629image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:54.835298image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:55.956407image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:57.236153image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:58.416306image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:59.210622image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:09:00.399544image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:50.347805image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:51.241885image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:52.583120image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:53.799398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:54.941924image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:56.070003image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:57.359909image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:58.492675image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:59.288825image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:09:00.503078image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:50.429108image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:51.350267image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:52.696614image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:53.904942image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:55.041806image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:56.176355image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:57.465679image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:58.570136image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:59.358637image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:09:00.625854image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:50.516430image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:51.466117image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:52.843246image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:54.022037image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:55.151037image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:56.288702image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:57.583636image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:58.647840image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:59.454192image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:09:00.758812image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:50.611459image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:51.592529image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:52.971360image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:54.136955image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:55.259589image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:56.421758image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:57.709741image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:58.725153image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:59.563400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:09:00.880934image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:50.709780image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:51.698703image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:53.094784image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:54.260384image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:55.372678image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:56.521696image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:57.829500image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:58.805020image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:59.683995image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:09:01.006674image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:50.793367image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:51.808062image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:53.221635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:54.364263image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:55.482196image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:56.630707image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:57.947281image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:58.888834image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-11-30T20:08:59.804614image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-11-30T20:09:08.049953image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ActiveCasesContinentDeaths/1M popNewCasesNewDeathsNewRecoveredPopulationSerious,CriticalTests/1M popTot Cases/1M popTotalCasesTotalDeathsTotalRecoveredTotalTestsWHO Region
ActiveCases1.0000.1050.3941.0001.0001.0000.6980.8840.0300.4630.9450.8690.8930.7330.140
Continent0.1051.0000.1611.0001.0001.0000.0000.0000.0980.1580.0710.0990.1010.0470.713
Deaths/1M pop0.3940.1611.0001.0001.0001.000-0.1320.4440.5330.8770.4860.5700.4620.2710.090
NewCases1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
NewDeaths1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
NewRecovered1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Population0.6980.000-0.1321.0001.0001.0001.0000.614-0.272-0.1230.7150.6560.6970.7130.178
Serious,Critical0.8840.0000.4441.0001.0001.0000.6141.0000.2360.4870.8780.8010.8440.7070.098
Tests/1M pop0.0300.0980.5331.0001.0001.000-0.2720.2361.0000.6300.1820.1630.2050.4110.137
Tot Cases/1M pop0.4630.1580.8771.0001.0001.000-0.1230.4870.6301.0000.5400.4680.5300.3600.147
TotalCases0.9450.0710.4861.0001.0001.0000.7150.8780.1820.5401.0000.9320.9870.8360.102
TotalDeaths0.8690.0990.5701.0001.0001.0000.6560.8010.1630.4680.9321.0000.9110.7350.000
TotalRecovered0.8930.1010.4621.0001.0001.0000.6970.8440.2050.5300.9870.9111.0000.8440.106
TotalTests0.7330.0470.2711.0001.0001.0000.7130.7070.4110.3600.8360.7350.8441.0000.082
WHO Region0.1400.7130.0901.0001.0001.0000.1780.0980.1370.1470.1020.0000.1060.0821.000

Missing values

2025-11-30T20:09:01.206079image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-30T20:09:01.651469image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-30T20:09:01.908219image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Country/RegionContinentPopulationTotalCasesNewCasesTotalDeathsNewDeathsTotalRecoveredNewRecoveredActiveCasesSerious,CriticalTot Cases/1M popDeaths/1M popTotalTestsTests/1M popWHO Region
0USANorth America3.311981e+085032179NaN162804.0NaN2576668.0NaN2292707.018296.015194.0492.063139605.0190640.0Americas
1BrazilSouth America2.127107e+082917562NaN98644.0NaN2047660.0NaN771258.08318.013716.0464.013206188.062085.0Americas
2IndiaAsia1.381345e+092025409NaN41638.0NaN1377384.0NaN606387.08944.01466.030.022149351.016035.0South-EastAsia
3RussiaEurope1.459409e+08871894NaN14606.0NaN676357.0NaN180931.02300.05974.0100.029716907.0203623.0Europe
4South AfricaAfrica5.938157e+07538184NaN9604.0NaN387316.0NaN141264.0539.09063.0162.03149807.053044.0Africa
5MexicoNorth America1.290662e+084626906590.050517.0819.0308848.04140.0103325.03987.03585.0391.01056915.08189.0Americas
6PeruSouth America3.301632e+07455409NaN20424.0NaN310337.0NaN124648.01426.013793.0619.02493429.075521.0Americas
7ChileSouth America1.913251e+07366671NaN9889.0NaN340168.0NaN16614.01358.019165.0517.01760615.092022.0Americas
8ColombiaSouth America5.093626e+07357710NaN11939.0NaN192355.0NaN153416.01493.07023.0234.01801835.035374.0Americas
9SpainEurope4.675665e+07354530NaN28500.0NaNNaNNaNNaN617.07582.0610.07064329.0151087.0Europe
Country/RegionContinentPopulationTotalCasesNewCasesTotalDeathsNewDeathsTotalRecoveredNewRecoveredActiveCasesSerious,CriticalTot Cases/1M popDeaths/1M popTotalTestsTests/1M popWHO Region
199New CaledoniaAustralia/Oceania285769.022NaNNaNNaN22.0NaN0.0NaN77.0NaN11099.038839.0NaN
200LaosAsia7285750.020NaNNaNNaN19.0NaN1.0NaN3.0NaN29374.04032.0WesternPacific
201DominicaNorth America72004.018NaNNaNNaN18.0NaN0.0NaN250.0NaN1005.013958.0Americas
202Saint Kitts and NevisNorth America53237.017NaNNaNNaN16.0NaN1.0NaN319.0NaN1146.021526.0Americas
203GreenlandNorth America56780.014NaNNaNNaN14.0NaN0.0NaN247.0NaN5977.0105266.0Europe
204MontserratNorth America4992.013NaN1.0NaN10.0NaN2.0NaN2604.0200.061.012220.0NaN
205Caribbean NetherlandsNorth America26247.013NaNNaNNaN7.0NaN6.0NaN495.0NaN424.016154.0NaN
206Falkland IslandsSouth America3489.013NaNNaNNaN13.0NaN0.0NaN3726.0NaN1816.0520493.0NaN
207Vatican CityEurope801.012NaNNaNNaN12.0NaN0.0NaN14981.0NaNNaNNaNEurope
208Western SaharaAfrica598682.010NaN1.0NaN8.0NaN1.0NaN17.02.0NaNNaNAfrica